Comparison between two model-based algorithms for Li-ion battery SOC estimation in electric vehicles

► A nonlinear battery model is identified for Li-ion battery state estimation. ► Two SOC-estimation filters using the established model are compared. ► The novel robust filter has a smaller error bound than does the standard filter. ► The novel robust filter has a better robustness against system no...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Simulation modelling practice and theory Jg. 34; S. 1 - 11
Hauptverfasser: Hu, Xiaosong, Sun, Fengchun, Zou, Yuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.05.2013
Schlagworte:
Algorithms
Battery modeling
Electric batteries
Electric vehicles
Errors
Extended Kalman filter
Lithium-ion batteries
Lithium-ion battery
Noise
Robustness
State estimation
Lithium-ion battery
Electric vehicles
Battery modeling
State estimation
ISSN:1569-190X, 1878-1462
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:► A nonlinear battery model is identified for Li-ion battery state estimation. ► Two SOC-estimation filters using the established model are compared. ► The novel robust filter has a smaller error bound than does the standard filter. ► The novel robust filter has a better robustness against system noise statistics. Accurate battery State of Charge (SOC) estimation is of great significance for safe and efficient energy utilization for electric vehicles. This paper presents a comparison between a novel robust extended Kalman filter (REKF) and a standard extended Kalman filter (EKF) for Li-ion battery SOC indication. The REKF-based method is formulated to explicitly compensate for the battery modeling uncertainty and linearization error often involved in EKF, as well as to provide robustness against the battery system noise to some extent. Evaluation results indicate that both filters have a good average performance, given appropriate noise covariances, owing to a small average modeling error. However, in contrast, the REKF-based SOC estimation method possesses slightly smaller root-mean-square (RMS) error. In the worst case, the robustness characteristics of the REKF result in an obviously smaller error bound (around by 1%). Additionally, the REKF-based approach shows superior robustness against the noise statistics, leading to a better tolerance to inappropriate tuning of the process and measurement noise covariances.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1569-190X
1878-1462
DOI:10.1016/j.simpat.2013.01.001